Adaptive Spiking Gating Multi-Scale Liquid State Machine for Orbital Maneuver Detection

Orbital maneuver detection is a core component of space situational awareness. The multi-scale characteristics of satellite orbital behavior and sample imbalance issues lead to challenges in existing methods, including insufficient feature adaptation and limited detection accuracy. This paper proposes an Adaptive Spiking Gating Multi-Scale Liquid State Machine (ASG-MSLSM) for orbital maneuver detection based on variations in satellite orbital parameters. The method integrates multi-scale reservoir pools with different scale-dependent decay factors and Leaky Integrate-and-Fire (LIF) neurons to enhance multi-scale temporal feature extraction capability. A spiking gating network is designed to adaptively learn fusion weights for multi-scale features, replacing traditional fixed equal-weight fusion strategies. During training, weighted binary cross-entropy loss is employed to address class imbalance. Experimental results based on real satellite data demonstrate that the proposed method significantly outperforms baseline models in maneuver detection metrics, achieving higher recall, improving feature separability, and reducing both missed detections and false alarms. These results indicate that the proposed method provides a robust solution for orbital maneuver detection.